Open AI, owners of popular chatbox, ChatGPT, released a 32-page document that detailed how to build an AI agents, and I am here to give you an over view on what you need to kickstart your journey.

But first, what is AI agents? For starters, Ai agents aren’t just smart chatbots or automated scripts; they’re capable systems that perform complex tasks on your behalf. But how exactly do you build one?

This guide breaks down the steps and key concepts from OpenAI’s Practical Guide to Building Agents to help you get started.

What is an AI Agent?

An AI agent is a system powered by a large language model (LLM), like GPT-4, that can carry out multi-step tasks independently. Unlike basic chatbots that respond to single prompts, agents understand workflows—sequences of actions required to achieve a goal—and execute them without constant human input.

“Agents are systems that independently accomplish tasks on your behalf.” — OpenAI Guide

Think of it as a virtual assistant that doesn’t just answer questions but actually gets things done—like analyzing documents, making decisions, sending emails, or updating records.

When Should You Use an Agent?

Agents shine in scenarios where traditional automation struggles. According to the guide, consider building an agent if:

• The task requires complex decision-making (e.g. refund approvals).
• The existing system has too many hard-to-manage rules.
• The process involves unstructured data, such as documents or natural language input.

For example, a rule-based system might flag payments over a certain amount for fraud, but an agent could evaluate a wide range of contextual clues to make a smarter decision.

Core Components of an AI Agent

OpenAI defines three main building blocks for an agent:

1. Model

The LLM that powers your agent’s intelligence. It handles reasoning, conversation, and decision-making. You might start with the most capable model and then switch to smaller, faster ones to save costs.

“Not every task requires the smartest model… Harder tasks may benefit from a more capable model.” — OpenAI Guide

2. Tools

External functions or APIs your agent uses to interact with the world—fetching data, sending emails, or updating systems. There are three types:

• Data tools (to retrieve information),
• Action tools (to perform tasks),
• Orchestration tools (to manage or delegate tasks to other agents).

3. Instructions

These are guidelines that define your agent’s role and behavior. For instance, if building a weather bot, the instructions could be:

“You are a helpful agent who can talk to users about the weather.”

How It Comes Together (Simplified Example in Code)

Using OpenAI’s SDK, here’s a basic setup:

weather_agent = Agent(
   name="Weather agent",
   instructions="You are a helpful agent who can talk to users about the weather.",
   tools=[get_weather],
)

This simple agent can respond to weather queries by calling the get_weather tool.

Tips for Building Better Agents

• Start with clarity: Understand what problem your agent will solve.
• Test and evaluate: Use evaluations (evals) to measure performance.
• Optimize smartly: Begin with powerful models, then replace with lighter ones if possible.
• Use reusable tools: Well-documented and tested tools reduce bugs and speed up development.

My Final Thoughts

Building an AI agent isn’t just about connecting a chatbot to data. It involves designing a smart, decision-capable system that can execute tasks with minimal supervision. With the right model, tools, and instructions in place—and a clear understanding of when agents are useful—you’ll be well on your way to creating intelligent assistants that do more than talk: they act.

“After reading this guide, you’ll have the foundational knowledge you need to confidently start building your first agent.” — OpenAI Guide

Emmanuel Azubuike is the Chief Storytelling Officer (CSO) of Work Smart.